Cooperative Evaluation of the Cause of Urban Traffic Congestion via Connected Vehicles

We developed a distributed data mining system to elaborate on a decision concerning the cause of urban traffic congestion via emerging connected vehicle (CV) technology. We observe this complex phenomena through the interactions between vehicles exchanging messages via vehicle to vehicle communication. Results are based on real-time simulation generated scenarios extended from the real-world traffic travel and activity patterns simulation cologne scenario. We evaluate a voting procedure that provides deeper insights using cooperation between vehicles, belief functions (BF) aimed at improving representation of information and a data association technique (DAT) aimed at data mining and extracting the association rules from the exchanged messages. Methods are tested and compared using a microscopic urban mobility simulator, SUMO, and a network simulator, ns-2, for the simulation of communication between CVs. Compared with the back-propagation algorithm (BP) extensively used in the past literature, our performance evaluation shows that the proposed methods enhance the estimation of the cause of congestion by 48% for the proposed VP, 58% for the BF, 71% for the DAT, and 70% for β-DAT. The methods also enhance detection time from 7.09% to 10.3%, and β-DAT outperforms BP by approximately 1.25% less false alarms triggered by the network, which can be significant in the context of real-time decision making. We show that a market penetration rate between 63% and 75% is enough to ensure satisfactory performance.